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import torch |
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from PIL import Image |
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from ultralytics.models.yolo.segment import SegmentationPredictor |
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from ultralytics.utils import DEFAULT_CFG, checks |
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from ultralytics.utils.metrics import box_iou |
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from ultralytics.utils.ops import scale_masks |
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from .utils import adjust_bboxes_to_image_border |
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class FastSAMPredictor(SegmentationPredictor): |
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""" |
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FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics |
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YOLO framework. |
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This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It |
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adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single- |
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class segmentation. |
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""" |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
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"""Initializes a FastSAMPredictor for fast SAM segmentation tasks in Ultralytics YOLO framework.""" |
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super().__init__(cfg, overrides, _callbacks) |
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self.prompts = {} |
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def postprocess(self, preds, img, orig_imgs): |
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"""Applies box postprocess for FastSAM predictions.""" |
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bboxes = self.prompts.pop("bboxes", None) |
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points = self.prompts.pop("points", None) |
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labels = self.prompts.pop("labels", None) |
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texts = self.prompts.pop("texts", None) |
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results = super().postprocess(preds, img, orig_imgs) |
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for result in results: |
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full_box = torch.tensor( |
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[0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32 |
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) |
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boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape) |
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idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten() |
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if idx.numel() != 0: |
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result.boxes.xyxy[idx] = full_box |
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return self.prompt(results, bboxes=bboxes, points=points, labels=labels, texts=texts) |
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def prompt(self, results, bboxes=None, points=None, labels=None, texts=None): |
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""" |
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Internal function for image segmentation inference based on cues like bounding boxes, points, and masks. |
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Leverages SAM's specialized architecture for prompt-based, real-time segmentation. |
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Args: |
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results (Results | List[Results]): The original inference results from FastSAM models without any prompts. |
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bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format. |
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points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels. |
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labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background. |
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texts (str | List[str], optional): Textual prompts, a list contains string objects. |
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Returns: |
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(List[Results]): The output results determined by prompts. |
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""" |
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if bboxes is None and points is None and texts is None: |
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return results |
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prompt_results = [] |
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if not isinstance(results, list): |
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results = [results] |
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for result in results: |
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if len(result) == 0: |
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prompt_results.append(result) |
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continue |
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masks = result.masks.data |
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if masks.shape[1:] != result.orig_shape: |
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masks = scale_masks(masks[None], result.orig_shape)[0] |
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idx = torch.zeros(len(result), dtype=torch.bool, device=self.device) |
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if bboxes is not None: |
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bboxes = torch.as_tensor(bboxes, dtype=torch.int32, device=self.device) |
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bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes |
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bbox_areas = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0]) |
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mask_areas = torch.stack([masks[:, b[1] : b[3], b[0] : b[2]].sum(dim=(1, 2)) for b in bboxes]) |
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full_mask_areas = torch.sum(masks, dim=(1, 2)) |
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union = bbox_areas[:, None] + full_mask_areas - mask_areas |
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idx[torch.argmax(mask_areas / union, dim=1)] = True |
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if points is not None: |
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points = torch.as_tensor(points, dtype=torch.int32, device=self.device) |
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points = points[None] if points.ndim == 1 else points |
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if labels is None: |
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labels = torch.ones(points.shape[0]) |
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labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device) |
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assert len(labels) == len(points), ( |
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f"Excepted `labels` got same size as `point`, but got {len(labels)} and {len(points)}" |
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) |
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point_idx = ( |
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torch.ones(len(result), dtype=torch.bool, device=self.device) |
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if labels.sum() == 0 |
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else torch.zeros(len(result), dtype=torch.bool, device=self.device) |
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) |
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for point, label in zip(points, labels): |
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point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label) |
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idx |= point_idx |
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if texts is not None: |
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if isinstance(texts, str): |
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texts = [texts] |
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crop_ims, filter_idx = [], [] |
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for i, b in enumerate(result.boxes.xyxy.tolist()): |
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x1, y1, x2, y2 = (int(x) for x in b) |
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if masks[i].sum() <= 100: |
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filter_idx.append(i) |
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continue |
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crop_ims.append(Image.fromarray(result.orig_img[y1:y2, x1:x2, ::-1])) |
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similarity = self._clip_inference(crop_ims, texts) |
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text_idx = torch.argmax(similarity, dim=-1) |
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if len(filter_idx): |
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text_idx += (torch.tensor(filter_idx, device=self.device)[None] <= int(text_idx)).sum(0) |
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idx[text_idx] = True |
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prompt_results.append(result[idx]) |
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return prompt_results |
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def _clip_inference(self, images, texts): |
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""" |
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CLIP Inference process. |
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Args: |
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images (List[PIL.Image]): A list of source images and each of them should be PIL.Image type with RGB channel order. |
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texts (List[str]): A list of prompt texts and each of them should be string object. |
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Returns: |
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(torch.Tensor): The similarity between given images and texts. |
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""" |
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try: |
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import clip |
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except ImportError: |
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checks.check_requirements("git+https://github.com/ultralytics/CLIP.git") |
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import clip |
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if (not hasattr(self, "clip_model")) or (not hasattr(self, "clip_preprocess")): |
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self.clip_model, self.clip_preprocess = clip.load("ViT-B/32", device=self.device) |
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images = torch.stack([self.clip_preprocess(image).to(self.device) for image in images]) |
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tokenized_text = clip.tokenize(texts).to(self.device) |
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image_features = self.clip_model.encode_image(images) |
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text_features = self.clip_model.encode_text(tokenized_text) |
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image_features /= image_features.norm(dim=-1, keepdim=True) |
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text_features /= text_features.norm(dim=-1, keepdim=True) |
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return (image_features * text_features[:, None]).sum(-1) |
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def set_prompts(self, prompts): |
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"""Set prompts in advance.""" |
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self.prompts = prompts |
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